This application addresses broad Challenge Area: Enabling Technologies, Challenge Topic =06-HD-101, Improved Interfaces for Prostheses to Impact Rehabilitation Outcomes. Recent advances in decoding motor intentions and the technology to record populations of neural activity patterns, has created one of the most exciting research areas in neuroscience because this work holds the promise of restoring lost motor output to those who are paralyzed. The success of brain-controlled interfaces depends critically on the user's ability to modulate neural activity in a specific way. To date, subjects are trained with methods that are ad hoc. However, as more sophisticated devices are developed, the need for concurrent improvements in learning techniques is becoming critical. Based on our success with state-of-the-art brain-driven prosthetics, we are proposing a novel, model-driven training approach. With these interfaces, all behavior is driven directly by neural activity and we have an unparalleled opportunity to manipulate task difficulty and monitor performance. This will allow us to model the learning process, use a novel operator-computer shared-control algorithm and define set points to drive learning in a way that minimizes the amount of training needed to master control of complex, brain-controlled prosthetic devices. These methods are likely to generalize to motor rehabilitation of paralyzed individuals. Brain-controlled prosthetics provide a direct link between recorded neural activity and the movement of external devices. Subjects must learn to modulate their neural activity patterns to control these devices successfully. This proposal outlines a novel method to facilitate a learning method leading to the control of complex, brain-controlled interfaces that may be generalized to a wide range of therapeutic rehabilitation.